Protecting Two-Qubit Quantum States by π\pi-Phase Pulses

We study the state decay of two qubits interacted with a common harmonic oscillator reservoir. There are both decoherence error and the error caused by the amplitude change of the superradiant state. We show that frequent $\pi$-phase pulses can eliminate both typpes of errors therefore protect a two-qubit odd-parity state more effectively than the frequent measurement method. This shows that the the methods using dynamical decoupling and the quantum Zeno effects actually can give rather {\em different} results when the operation frequency is finite

Principals' sensemaking of coaching for ambitious reading instruction in a high-stakes accountability policy environment

In the present exploratory qualitative study we examine the contextual factors that influenced the implementation of a multi-year comprehensive literacy-coaching program (Content- Focused Coaching, CFC). We argue that principals' sensemaking of the dialogic instructional strategies promoted by the program in light of high-stakes accountability policies influenced coaches' work with teachers. Principals' views of the efficacy of the teaching strategies promoted by CFC for meeting accountability targets influenced how principals socially positioned coaches in schools (i.e., the degree to which they promoted coaches as sources of expertise to teachers), and the extent to which the coaching received by teachers focused on implementing dialogic teaching practices. Our results also suggest that principals' sensemaking of the program was for the most part consistent across years, even in the face of shifting accountability status and changes in district leadership. Implications of our findings for improving the implementation of coaching programs are discussed

Examining Distinctive Working Memory Profiles in Chinese Children With Predominantly Inattentive Subtype of Attention-Deficit/Hyperactivity Disorder and/or Reading Difficulties

Although evidence has shown that both RD and ADHD-I children suffer from working memory problems, inconsistencies in impaired modalities have been reported. This study aimed to (1) compare the three WM domains (i.e., verbal WM, visual-spatial WM, and behavioral WM) among pure ADHD-I, pure RD, comorbid ADHD-I+RD, and typical control groups and (2) examine the impact of comorbidity on the three WM domains. A Chinese sample of participants from Hong Kong included 29 children in the ADHD-I group, 78 children in the RD group, 31 children in the comorbid group (ADHD-I+RD), and 64 children in the TD control group. All participants completed the assessments individually. The findings showed that the children with ADHD-I and/or RD exhibited diverse cognitive profiles. In particular, RD was associated with verbal and visual-spatial working memory deficits, while ADHD-I was associated with behavioral working memory deficits. Interestingly, the comorbid condition demonstrated additive deficits of the two disorders but with greater deficits in behavioral working memory. These findings support the cognitive subtype hypothesis and provide a clearer picture of the distinctive working memory profiles of different groups, allowing for the development of intervention programs in the future

Resonant Subband Landau Level Coupling in Symmetric Quantum Well

Subband structure and depolarization shifts in an ultra-high mobility GaAs/Al_{0.24}Ga_{0.76}As quantum well are studied using magneto-infrared spectroscopy via resonant subband Landau level coupling. Resonant couplings between the 1st and up to the 4th subbands are identified by well-separated anti-level-crossing split resonance, while the hy-lying subbands were identified by the cyclotron resonance linewidth broadening in the literature. In addition, a forbidden intersubband transition (1st to 3rd) has been observed. With the precise determination of the subband structure, we find that the depolarization shift can be well described by the semiclassical slab plasma model, and the possible origins for the forbidden transition are discussed.Comment: 4 pages, 2 figure

Assessing students' skills at writing analytically in response to texts

Despite the importance of writing analytically in response to texts, there are few assessments measuring students' mastery of this skill. This manuscript describes the development of a response-to-text assessment (RTA) intended for use in research. In a subsequent validity investigation we examined whether the RTA distinguished among classrooms in students' ability to write analytically in response to text and whether measures of teaching predicted this variation. We demonstrate that the RTA was correlated with the state standardized assessment, but did not overlap with this accountability test completely and, additionally, that more variation between classrooms existed on the RTA. Students' opportunities for reasoning and extended writing in the classroom were significantly associated with RTA scores. The findings suggest that the RTA can be a valuable tool for conducting research on students' attainment of analytic writing skills and for understanding how teaching relates to student achievement on these skills. © 2013 by The University of Chicago. All rights reserved

Combining Multiple Measures of Students' Opportunities to Develop Analytic, Text-Based Writing Skills

Guided by evidence that teachers contribute to student achievement outcomes, researchers have been reexamining how to study instruction and the classroom opportunities teachers create for students. We describe our experience measuring students' opportunities to develop analytic, text-based writing skills. Utilizing multiple methods of data collection-writing assignment tasks, daily logs, and an annual survey-we generated a composite that was used in prediction models to examine multivariate outcomes, including scores on a state accountability test and a project-developed response-to-text assessment. Our findings demonstrate that students' opportunities to develop analytic, text-based writing skills predicted classroom performance on the project-developed response-to-text assessment. We discuss the importance of considering the measure(s) of learning when examining teaching-learning associations as well as implications for combining multiple measures for purposes of better construct representation. © 2012 Copyright Taylor and Francis Group, LLC

Geometry meets semantics for semi-supervised monocular depth estimation

Depth estimation from a single image represents a very exciting challenge in computer vision. While other image-based depth sensing techniques leverage on the geometry between different viewpoints (e.g., stereo or structure from motion), the lack of these cues within a single image renders ill-posed the monocular depth estimation task. For inference, state-of-the-art encoder-decoder architectures for monocular depth estimation rely on effective feature representations learned at training time. For unsupervised training of these models, geometry has been effectively exploited by suitable images warping losses computed from views acquired by a stereo rig or a moving camera. In this paper, we make a further step forward showing that learning semantic information from images enables to improve effectively monocular depth estimation as well. In particular, by leveraging on semantically labeled images together with unsupervised signals gained by geometry through an image warping loss, we propose a deep learning approach aimed at joint semantic segmentation and depth estimation. Our overall learning framework is semi-supervised, as we deploy groundtruth data only in the semantic domain. At training time, our network learns a common feature representation for both tasks and a novel cross-task loss function is proposed. The experimental findings show how, jointly tackling depth prediction and semantic segmentation, allows to improve depth estimation accuracy. In particular, on the KITTI dataset our network outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

Isolation and characterization of antimicrobial, anti-inflammatory and chemopreventive flavones from premna odorata blanco

Premna odorata Blanco (Verbenaceae) is a native tree of the Philippines where its leaves are used traditionally for vaginal irrigation and tuberculosis. It is one of the seven components of a commercialized Philippine herbal preparation called "Pito-Pito". Its medicinal uses, however, have not been scientifically validated. This tree is not commonly cultivated and thrive in the less accessible limestone forests of the Philippines. Solvent partitioning and fractionation of the ethanolic crude extract of the leaves isolated two yellow amorphous powders. The identities of these compounds were determined by LC/MS/MS and NMR spectroscopic analyses, and their spectra were compared with literature data. The isolates were flavone aglycones which were the widespread acacetin and the nonwidespread diosmetin. These flavones were isolated from the P. odorata for the first time ever. They had been reported by earlier studies to exhibit medicinal properties as antimicrobial, anti-inflammatory and chemopreventive. Thus, the current study has provided a scientific evidence of the medicinal properties of the leaves of P. odorata that could become the popular basis for the plant's sustainable use, conservation and cultivation. © 2011 Academic Journals.published_or_final_versio